• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.675-686
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• 2005

In this paper, we propose an efficient face region detection technique for the content-based video summarization. To segment video, shot changes are detected from a video sequence and key frames are selected from the shots. We select one frame that has the least difference between neighboring frames in each shot. The proposed face detection algorithm detects face region from selected key frames. And then, we provide user with summarized frames included face region that has an important meaning in dramas or movies. Using Bayes classification rule and statistical characteristic of the skin pixels, face regions are detected in the frames. After skin detection, we adopt the projection method to segment an image(frame) into face region and non-face region. The segmented regions are candidates of the face object and they include many false detected regions. So, we design a classifier to minimize false lesion using CART. From SGLD matrices, we extract the textual feature values such as Inertial, Inverse Difference, and Correlation. As a result of our experiment, proposed face detection algorithm shows a good performance for the key frames with a complex and variant background. And our system provides key frames included the face region for user as video summarized information.

• Journal of the Korean Recycled Construction Resources Institute
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• v.8 no.2
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• pp.227-234
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• 2020

Under consideration of construction waste recycling, candidate secondary products applicable for the civil and construction areas can be reliably employed based on the recycling cost analysis. For the validation purpose, probability-based cost analyses were performed to estimate recycling cost profit considering uncertainties. When recycling construction wastes, the costs at each stage are fully dependent on target products to be adopted. To achieve commercialization of each product, its quality has to be improved with economic efficiency through accurate evaluation of input costs. Based on the probabilistic recycling cost analysis, the cost benefit for target products was estimated with waste classification cost, transportation cost to recycling treatment facilities and production cost. All necessary information on the cost analysis were collected from literature, disclosure, and existing recycling companies. In addition, a cost difference between recycled and non-recycled events was made. As a result, a probability-based recycling cost estimate for candidate secondary products was herein presented.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.1
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• pp.23-31
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• 2012

Measures against non-point sources pollution in Saemangeum watershed should be established to control water quality of Saemangeum lake, because non-point sources pollution discharge portions of BOD (Biological Oxygen Demand) and TP (Total Phosphorous) in the watershed were 68.4 and 61.4%, respectively. In this study, target regions for the non-point sources pollution control were selected to apply BMP (Best Management Practices) for the agricultural area of Saemanguem watershed in terms of TP that caused eutrophication at the lake. Target regions were selected by the NPSI (Non-point source index) that was calculated by the total 12 indexes at the steps of non-point source production, emission and outflow. Weights of the indexes were determined by the watershed management experts oriented AHP (Analytic Hierarchy Process) analysis. The target region was selected at the unit of Korean basic administrative district 'Dong/Li'. At the results of NPSI calculations through the GIS (Geographical Information System) tools, two sets of 5 regions were selected in the Man-kyung River and Dong-gin River. The main reason for the selected target regions was livestock activity in the district. The results of this study can be useful for implementing the reduction projects of agricultural non-point sources pollution to control water quality in Saemangeum lake.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.137-148
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• 2009

The purpose of this study is to implement variable selection algorithm which helps construct a reliable linear regression model. If we use all candidate variables to construct a linear regression model, the significance of the model will be decreased and it will cause 'Curse of Dimensionality'. And if the number of data is less than the number of variables (dimension), we cannot construct the regression model. Due to these problems, we consider the variable selection problem as a combinatorial optimization problem, and apply GA (Genetic Algorithm) to the problem. Typical measures of estimating statistical significance are $R^2$, F-value of regression model, t-value of regression coefficients, and standard error of estimates. We design GA to solve multi-objective functions, because statistical significance of model is not to be estimated by a single measure. We perform experiments using simulation data, designed to consider various kinds of situations. As a result, it shows better performance than LARS (Least Angle Regression) which is an algorithm to solve variable selection problems. We modify algorithm to solve portfolio selection problem which construct portfolio by selecting stocks. We conclude that the algorithm is able to solve real problems.

• Journal of KIISE:Software and Applications
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• v.36 no.8
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• pp.623-632
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• 2009

In this paper, we propose a pulmonary nodule registration for the tracking of lung nodules in sequential CT scans. Our method consists of following five steps. First, a translational mismatch is corrected by aligning the center of optimal bounding volumes including each segmented lung. Second, coronal maximum intensity projection(MIP) images including a rib structure which has the highest intensity region in baseline and follow-up CT series are generated. Third, rigid transformations are optimized by normalized average density differences between coronal MIP images. Forth, corresponding nodule candidates are defined by Euclidean distance measure after rigid registration. Finally, template matching is performed between the nodule template in baseline CT image and the search volume in follow-up CT image for the nodule matching. To evaluate the result of our method, we performed the visual inspection, accuracy and processing time. The experimental results show that nodules in serial CT scans can be rapidly and correctly registered by coronal MIP-based rigid registration and local template matching.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.1
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• pp.70-78
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• 2013

Before GBAS ground systems is installed at the airport, the site survey is needed to determine the suitability of proposed GBAS candidate sites depending on the siting requirements. Therefore, analysis of GPS signal reception environment, one of the site survey steps, is required. In this paper, the number of visible satellites, GPS signal strength, multipath error, radio frequency interference and predicted availability were analyzed using the GPS data of Gimpo International Airport measured by PortaSAT equipments and the analysis results were represented.

• Journal of Energy Engineering
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• v.14 no.4 s.44
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• pp.219-225
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• 2005

for the removal of carbon dioxide in flue gas, which is the main cause of the global warming, chemical absorption methods were widely used for years. Alkanolamines such as MEA, AMP, MDEA are mainly used as an absorbent. In this study, cyclic amines - Piperazine, Piperidine and Cyclohelylamine are investigated to compare the chemical solubilities, absorption capacities and rate ot reaction. In conclusion, Piperazine is the most effective absorbent of $CO_2$ and ran be the excellent alternative to the former absorbents. Experiments were performed at 5, 10, 15 weight percent, 30, 40, $50^{\circ}C$, and the gas solubilities are determined by back-titration method, and using Gas Chromatography the absorption rate was compared.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.341-348
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• 2016

HEVC is the next compression standard and is expected to be used widely replacing the conventional H.264/AVC standard. The compression ratio of the HEVC is twice times than H.264/AVC, whereas its computational complexity is increased by up to 40%. Many research efforts have been made to reduce the computational complexity and to speed up encoding. For intra coding, the rough mode decision (RMD) is commonly applied. The rate-distortion optimization (RDO) process to decide the best mode is too complex so that RMD chooses the candidate modes with a simple process and sends the candidates to RDO process. However, for large-size blocks, the RMD also requires considerable computations. In this paper, a down-sampling scheme is proposed for the RMD process. The reference pixel loading, predicted pixel generation are performed using the down-sampled pixel data. When the proposed scheme is applied to the RMD, the computational complexity is reduced by 70% with a marginal bitrate increase of 0.04%. In terms of area of hardware-based RMD, the gate count and the buffer size is reduced 33% and 66%, respectively.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.91-101
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• 2001

It has been shown that the multiple trees predictors are more accurate in reducing test set error than a single tree predictor. There are two ways of generating multiple trees. One is to generate modified training sets by resampling the original training set, and then construct trees. It is known that arcing algorithm is efficient. The other is to perturb randomly the working split at each node from a list of best splits, which is expected to generate reasonably good trees for the original training set. We propose a new combined multiple regression trees predictor which uses the latter multiple regression tree predictor as a predictor based on a modified training set at each stage of arcing. The efficiency of those prediction methods are compared by applying to high throughput screening of chemical compounds for biological effects.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1178-1188
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• 2003

In this paper, we describe image mosaicing techniques for constructing a large high-resolution image with images taken by a video camera in hand. we propose the method which is automatically retrieving the exact matching area using color information and shape information. The proposed method extracts first candidate areas which have similar form using a Voronoi Distance Matching Method which is rapidly estimating the correspondent points between adjacent images, and calculating initial transformations of them and finds the final matching area using color information. It is a method that creates Voronoi Surface which set the distance value among feature points and other points on the basis of each feature point of a image, and extracts the correspondent points which minimize Voronoi Distance in matching area between an input image and a basic image using the binary search method. Using the Levenberg-Marquadt method we turn an initial transformation matrix to an optimal transformation matrix, and using this matrix combine a basic image with a input image.